The FIM section is continuing an effort to develop more robust, informative, and quantitative methods for mapping human brain function in both the activated state and during resting state. To summarize, the following were among the highlights, organized by research project corresponding to the listed post doc:[unreadable] [unreadable] Rasmus Birn[unreadable] Respiration Response Function[unreadable] Changes in the subjects breathing rate and depth can cause significant fMRI signal changes. This can decrease the detection power of functional activation, or lead to false positives, particularly in functional connectivity analyses. Previously, fMRI signal changes in response to cued breathing variations, such as breath holding, have been modeled using the typical activation-induced hemodynamic response functions (HRF), or by shifting a boxcar waveform by 6s. A more accurate model of the respiration induced signal changes to breath holding, as well as to cued depth or rate changes, can be obtained by deriving a new respiration response function from the average fMRI response to a single deep breath. This response is much slower than the typical activation-induced HRF. It accurately fits the average signal change induced by variations in respiration rate and depth, but considerable variation in the latency and shape of the response across the brain remains, which can be accounted for as a first approximation by shifting the latency of the modeled respiration response. [unreadable] [unreadable] Calibration[unreadable] The amplitude of the BOLD fMRI response depends strongly on the underlying vasculature, with the largest responses generally occurring in large draining veins. This makes it difficult to accurately determine subtle differences in the amount of neuronal activity between brain regions or between subjects. Earlier studies have suggested calibrating the BOLD signal by using a hypercapnic challenge, achieved either by an administration of CO2 or by breath-holding, which can provide a map of the relative changes in signal from a global increase in blood flow. A similar calibration of the BOLD response, however, can be obtained by having the subject perform cued breathing depth or rate changes, or simply by measuring the normal and spontaneous variations in breathing during rest.[unreadable] [unreadable] David Knight[unreadable] During Pavlovian fear conditioning a conditioned stimulus (CS) is repeatedly paired with an aversive unconditioned stimulus (UCS). In many studies the CS and UCS are paired on every trial, whereas in others the CS and UCS are paired intermittently. To better understand the influence of the CS-UCS pairing rate on brain activity, this study presented continuously, intermittently, and non-paired CSs during fear conditioning. Amygdala, anterior cingulate, and fusiform gyrus activity increased linearly with the CS-UCS pairing rate. In contrast, insula and left dorsolateral prefrontal cortex responses were larger during intermittently paired CS presentations relative to continuously and non-paired CSs. These results demonstrate two distinct patterns of activity in disparate brain regions. Amygdala, anterior cingulate, and fusiform gyrus activity paralleled the CS-UCS pairing rate, whereas the insula and doroslateral prefrontal cortex appeared to respond to the uncertainty inherent in intermittent CS-UCS pairing procedures. These findings may further clarify the role of these brain regions in Pavlovian fear conditioning.[unreadable] [unreadable] Niko Kriegeskorte[unreadable] High-resolution functional magnetic resonance imaging (hi-res fMRI) promises to help bridge the gap of spatial scales between human low-resolution neuroimaging and animal invasive electrophysiology. We have explored how the fine-scale neuronal-pattern information present in hi-res fMRI data can be exploited for neuroscientific insight by means of multivariate analysis. Our focus has been on developing the novel approach of representational similarity analysis, which allows us (1) to combine evidence across brain space and experimental conditions to sensitively detect neuronal pattern information and (2) to relate results (a) between different modalities of brain-activity measurement, (b) between different species, and (c) between brain-activity data and computational models of brain information processing. This approach has been applied to comparing human and monkey data from hi-res fMRI and single-cell recordings, respectively. We investigated response patterns elicited by the same 92 photographs of isolated natural objects in inferotemporal (IT) cortex of both species. Within each species, we computed a matrix of response-pattern similarities (one similarity value for each pair of images). We found a striking match of the resulting similarity matrices for man and monkey. This finding suggests very similar categorical IT representations and provides some hope that data from single-cell recording and fMRI, for all their differences, may consistently reveal neuronal representations when subjected to massively multivariate analyses of response-pattern information.[unreadable] [unreadable] Anthony Boemio[unreadable] Contemporary models of speech perception assume an initial level of processing at which the auditory representation of speech is transformed to a format enabling contact with stored linguistic units such as words. However, details of the underlying neural mechanisms have not been specified. Using fMRI and a novel acoustic stimulus possessing graded intelligibility, we obtained evidence supporting the existence of combination-sensitive neurons at the third auditory cortical synapse. Through a process of feed-forward conjunction, combination-sensitive neurons integrate low-level acoustic features such as tones and frequency sweeps extracted at earlier cortical levels thus forming transient representations of critical acoustic correlates of articulatory movements. Moreover, combination-sensitive neurons may be involved in the recognition of other[unreadable] familiar sounds as well.[unreadable] [unreadable] Kevin Murphy[unreadable] Accurate characterization of the BOLD HRF is important to understand neuro-vascular coupling. Rather than returning to baseline values, we have consistently observed a post undershoot rebound effect associated with a very brief stimulus (23ms). The characteristics of this rebound were probed by modifying various parameters: 1) Scan resolution was increased and shown to strengthen the effect; 2) Inserting an ISI of 60s between stimuli established that the response function continues longer than 20sec with fluctuations on the order of 0.1Hz; 3) By presenting stimuli in a rapid event-related jittered design, the existence of the rebound was demonstrated in the case of overlapping IRFs; 4) Varying the stimulus duration modulated the ratio of rebound to overshoot response. The presence of the rebound response might reflect a lack of time for a feedback loop in the vasculature, thus setting up oscillations which may be the cause of low-frequency resting state fluctuations. The visibility of 0.1Hz fluctuations beyond 20s after stimulus onset might also suggest that the phases of these resting state fluctuations are reset by the event and thus visible after averaging.